Abstract: In recent years,with the rapid development of Internet services,fraudulent activities have become increasingly prevalent and are continuously evolving in their patterns.Graph neural networks(GNNs),owing to their strong capability in modeling structural information,have been widely applied to fraud detection tasks.However,most existing methods are built upon the homophily assumption,which often does not hold in real-world fraud detection scenarios.This is because fraudulent behaviors are inherently heterophilic－fraudsters tend to camouflage themselves by connecting to benign nodes.Meanwhile,severe class imba-lance among nodes is commonly observed,whereas the imbalance in edge relations and the exploitation of their latent information have received limited attention.To address these challenges,a fraud detection framework is proposed for imbalanced and heterophilic graphs.Specifically,a learnable dual-channel graph convolution filter adaptively aggregates low- and high-frequency signals from neighbors,enabling effective modeling of node features across homophilic and heterophilic edges.Additionally,label-aware node and edge samplers mitigate graph imbalance,and sampled edges serve as auxiliary supervision to guide filter training.Experi-mental results on four real-world datasets demonstrate that the proposed method outperforms representative baselines in fraud detection and model robustness.

Key words: Fraud detection, Dual-channel message passing, Graph neural networks, Class imbalance, Label-aware node and edge sampling